US8650016B2 - Multiscale finite volume method for reservoir simulation - Google Patents
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Definitions
- the present invention generally relates to simulators for characterizing subsurface reservoirs, and more particularly, to simulators that use multiscale methods to simulate fluid flow within subsurface reservoirs.
- Natural porous media such as subsurface reservoirs containing hydrocarbons
- High-resolution geological models which often are composed of millions of grid cells, are generated to capture the detail of these reservoirs.
- Current reservoir simulators are encumbered by the level of detail available in the fine-scale models and direct numerical simulation of subsurface fluid flow on the fine-scale is usually not practical.
- Various multiscale methods which account for the full resolution of the fine-scale geological models, have therefore been developed to allow for efficient fluid flow simulation.
- Multiscale methods include multiscale finite element (MSFE) methods, mixed multiscale finite element (MMSFE) methods, and multiscale finite volume (MSFV) methods. All of these multiscale methods can be applied to compute approximate solutions at reduced computational cost. While each of these methods reduce the complexity of a reservoir model by incorporating the fine-scale variation of coefficients into a coarse-scale operator, each take a fundamentally different approach to constructing the coarse-scale operator.
- MSFE multiscale finite element
- MMSFE mixed multiscale finite element
- MSFV multiscale finite volume
- the multiscale finite volume (MSFV) method is based on a finite volume methodology in which the reservoir domain is partitioned into discrete sub-volumes or cells and the fluxes over the boundaries or surfaces of each cell are computed. Since the fluxes leaving a particular cell are equivalent to the fluxes entering an adjacent cell, finite volume methods are considered to be conservative. Thus, the accumulations of mass in a cell are balanced by the differences of mass influx and outflux. Accordingly, mass conservation is strictly honored by multiscale finite volume (MSFV) methods, which can be very important in some reservoir simulation applications such as when a mass conservative fine-scale velocity field is needed for multiphase flow and transport simulations.
- the multiscale finite element (MSFE) and mixed multiscale finite element (MMSFE) methods are based on a finite element scheme, which breaks the reservoir domain into a set of mathematical spaces commonly referred to as elements. Physical phenomenon within the domain is then represented by local functions defined over each element.
- MMSFE mixed multiscale finite element
- a multi-scale method for use in simulating a fine-scale geological model of a subsurface reservoir.
- the method includes providing a fine-scale geological model of a subsurface reservoir associated with a fine-scale grid having a plurality of fine-scale cells.
- the method includes defining a primary coarse-scale grid having a plurality of primary coarse-scale cells and a dual coarse-scale grid having a plurality of dual coarse-scale cells.
- the dual coarse-scale grid defines a portion of the fine-scale cells as internal, edge, and node cells.
- a coarse-scale operator is constructed based on the internal, edge, and node cells and pressure in the dual coarse-scale cells is computed using the coarse-scale operator.
- a conservative flux field is computed using the pressure in the dual coarse-scale cells while accounting for transverse fluxes between the dual coarse-scale cells such as between edge cells.
- a display is produced based on the conservative flux field.
- the display can include a representation of pressure distributions, velocity fields, and fluid flow within the subsurface reservoir.
- the edge cells can be fine-scale cells having an interface, which is a transition between adjacent dual coarse-scale cells, traversing therethrough.
- the node cells can be fine-scale cells having portions of at least two interfaces traversing therethrough.
- the internal cells can be fine-scale cells free of an interface between adjacent dual coarse-scale cells.
- the conservative flux field is computed using iteratively solved local boundary conditions.
- the transverse fluxes are computed based on a previous pressure solution in the dual coarse-scale cells.
- the transverse fluxes are computed from local solutions on the primary coarse-scale grid.
- the transverse fluxes are computed using a relaxation parameter.
- the relaxation parameter can be computed based on residual histories.
- the relaxation parameter is optimized based on sets of successive residuals.
- the coarse-scale operator is stabilized using a Krylov-subspace accelerator.
- the coarse-scale operator is stabilized using a smoothing operator.
- Another aspect of the present invention includes a multi-scale method for use in simulating a fine-scale geological model of a subsurface reservoir.
- the method includes providing a fine-scale geological model of a subsurface reservoir associated with a fine-scale grid having a plurality of fine-scale cells.
- the method includes defining a primary coarse-scale grid having a plurality of primary coarse-scale cells.
- the method includes defining a dual coarse-scale grid having a plurality of dual coarse-scale cells such that adjacent dual coarse-scale cells form an interface that traverses some of the fine-scale cells.
- the fine-scale cells that are traversed by a single interface are defined as edge cells.
- the fine-scale cells that are traversed by portions of at least two interfaces are defined as node cells.
- the fine-scale cells that are free of an interface are defined as internal cells.
- Pressure is computed in the dual coarse-scale cells.
- a conservative flux field is computed using the pressure in the dual coarse-scale cells while accounting for transverse fluxes between the dual coarse-scale cells such as between edge cells.
- a display is produced based on the conservative flux field.
- the display can include a representation of pressure distributions, velocity fields, and fluid flow within the subsurface reservoir.
- the conservative flux field is computed using iteratively solved local boundary conditions.
- the transverse fluxes are computed based on a previous pressure solution in the dual coarse-scale cells.
- the transverse fluxes are computed from local solutions on the primary coarse-scale grid.
- the transverse fluxes are computed using a relaxation parameter.
- the relaxation parameter can be computed based on residual histories.
- the relaxation parameter is optimized based on sets of successive residuals.
- the coarse-scale operator is stabilized using a Krylov-subspace accelerator.
- the coarse-scale operator is stabilized using a smoothing operator.
- the system includes a database, computer processor, a software program, and a visual display.
- the database is configured to store data such as fine-scale geological models, fine-scale grids, primary coarse-scale grids, dual coarse-scale grids, and coarse-scale operators.
- the computer processor is configured to receive data from the database and execute the software program.
- the software program includes a coarse-scale operator module and a computation module.
- the coarse-scale operator module constructs coarse-scale operators.
- the computation module computes pressure in the dual coarse-scale cells using the coarse-scale operators.
- the computation module also computes conservative flux fields using the pressure in the dual coarse-scale cells while accounting for transverse fluxes between the dual coarse-scale cells such as between edge cells.
- the visual display can display system outputs such as pressure distributions, velocity fields, and simulated fluid flow within the subsurface reservoir.
- the software includes a coarse-scale operator module and a computation module.
- the coarse-scale operator module constructs coarse-scale operators based on internal cells, edge cells, and node cells defined on a fine-scale grid by a dual coarse-scale grid having a plurality of dual coarse-scale cells.
- the computation module computes pressure in the dual coarse-scale cells using the coarse-scale operator.
- the computation module also computes a conservative flux field using the pressure in the dual coarse-scale cells while accounting for transverse fluxes between the dual coarse-scale cells such as between edge cells.
- FIG. 1 is a schematic view of a two-dimensional fine-scale grid domain partitioned into internal, edge, and node point cells, in accordance with an aspect of the present invention.
- FIG. 3 is a graph showing a sparsity pattern of the multiscale finite volume matrix M for the fine-scale grid depicted in FIG. 1 .
- FIG. 6 illustrates a 100 ⁇ 100 fine-scale grid having a statistically isotropic permeability field and a quarter five spot well configuration.
- FIG. 7 is a graph of the convergence history of MSFV iterations for the isotropic permeability field illustrated in FIG. 6 .
- FIG. 8 illustrates a 100 ⁇ 100 fine-scale grid having a statistically anisotropic permeability field and a quarter five spot well configuration.
- FIG. 9 is a graph of the convergence history of MSFV iterations for the anisotropic permeability field illustrated in FIG. 8 .
- FIG. 10 is a graph of the convergence histories of MSFV iterations for the anisotropic permeability field illustrated in FIG. 8 using various smoothers.
- FIG. 11 is a graph of the convergence histories of MSFV iterations for the anisotropic permeability field illustrated in FIG. 8 using the LR smoother with various smoothing steps.
- FIG. 12 is a graph of the convergence histories of MSFV iterations for a homogeneous permeability field for various grid aspect ratios.
- FIG. 13 is a graph of the convergence histories of MSFV iterations for a homogeneous permeability field for various grid aspect ratios and using the LR smoother.
- FIG. 14A illustrates a heterogeneous permeability field consisting of multiple shale layers.
- FIG. 14B is a graph of the convergence histories of MSFV iterations for the heterogeneous permeability field illustrated in FIG. 14A for various smoothers.
- FIG. 14C is a schematic showing the approximate pressure solution for the heterogeneous permeability field illustrated in FIG. 14A using the original MSFV method.
- FIG. 14D is a schematic showing the converged pressure solution for the heterogeneous permeability field illustrated in FIG. 14A in accordance with an aspect of the present invention.
- FIG. 15 illustrates a permeability field from a SPE test case (top figure), the approximate pressure solution using the original MSFV method (middle figure), and the converged pressure solution in accordance with an aspect of the present invention (bottom figure).
- FIG. 16 is a graph of the convergence histories of MSFV iterations for a permeability field from a SPE test case for various smoothers.
- FIG. 17 is a graph of the convergence histories of MSFV iterations for a permeability field from a SPE test case for various smoothers.
- FIG. 18 is a graph of the convergence histories of MSFV iterations for a permeability field from a SPE test case.
- FIG. 19 is a graph of the convergence histories of MSFV iterations for a permeability field from a SPE test case using the DAS smoother.
- FIG. 20 is a schematic diagram of a system that can perform a multiscale finite volume method, in accordance with the present invention.
- Embodiments of the present invention describe methods that utilize multiscale physics and are applied to simulation of fluid flow within a subterranean reservoir. Modeling flow and transport in geological porous media is important in many energy-related and environmental problems, including reservoir simulation, CO2 sequestration, and management of water resources. Since the flow field, which is necessary to solve the transport equation, is dictated by permeability—a highly heterogeneous medium property, applications typically require solving problems with many degrees of freedom and highly heterogeneous coefficients.
- a Multiscale Finite Volume (MSFV) method has been developed to efficiently solve large heterogeneous problems (elliptic or parabolic); it is usually employed for pressure equations and delivers conservative flux fields to be used in transport problems.
- the method essentially relies on the hypothesis that the fine-scale problem can be reasonably described by a set of local solutions coupled by a conservative global (coarse-scale) problem. In most cases, the boundary conditions assigned for the local problems are satisfactory and the approximate conservative fluxes provided by the method are accurate without iterative improvements.
- a procedure or method is provided to iteratively improve the boundary conditions of the local problems.
- the method is responsive to the data structure of the underlying MSFV method and employs a Krylov-subspace projection method to obtain an unconditionally stable scheme and accelerate convergence.
- the MSFV operator is used.
- the MSFV operator is combined in a two step method with an operator derived from the problem solved to construct the conservative flux field.
- the resulting iterative MSFV algorithms or methods allow arbitrary reduction of the solution error without compromising the construction of a conservative flux field, which is guaranteed at any iteration. Since it converges to the exact solution, the method can be regarded as a linear solver.
- the embodiments can advantageously be viewed as preconditioned versions of the Generalized Minimal Residual method (GMRES), with a very peculiar beneficial characteristic that the residual on the coarse grid is zero at any iteration—thus conservative fluxes can be obtained.
- GMRES Generalized Minimal Residual method
- MSFV Multiscale Finite-Volume
- a natural and elegant approach is employed to iteratively improve the quality of the localization assumption: construct an iterative method based on the MSFV operator only, which can be stabilized by use of a Krylov-space accelerator.
- Eq. (1) represents an elliptic or parabolic problem satisfied by a scalar field (hereafter “pressure”) that determines a flux field (proportional to the pressure gradient) to be employed in a transport equation.
- pressure a scalar field
- flux field proportional to the pressure gradient
- FIG. 1 depicts the architecture of the multiscale finite volume method with a fine-scale grid 100 , a conforming primal coarse-scale grid 110 shown in bolded solid line, and a conforming dual coarse-scale grid 120 shown in dashed line.
- the fine-scale grid 100 includes of a plurality of fine-scale cells 130 .
- the primal coarse-scale grid 110 has primal coarse-scale cells 140 and is constructed on the fine-scale grid 100 such that each primal coarse-scale cell 140 is comprised of multiple fine-scale cells 130 .
- the dual coarse-scale grid 120 which also conforms to the fine-scale grid 100 , is constructed such that each dual coarse-scale cell 150 is comprised of multiple fine-scale cells 130 . For example in FIG.
- both the primal coarse-scale cells 140 and dual coarse-scale cells 150 contain 5 ⁇ 5 fine-scale cells 130 .
- the primal coarse-scale and dual coarse-scale grids, respectively 110 and 120 can be much coarser than the underlying fine grid 100 .
- the system and methods disclosed herein not limited to the simple grids shown in FIG. 1 , as very irregular grids or decompositions can be employed, as well as, other sized grids such as the coarse-scale and dual coarse-scale cells containing 7 ⁇ 7 or 11 ⁇ 11 fine-scale cells.
- transitions between adjacent dual coarse-scale cells 150 form interfaces 190 that overly and traverse the fine-scale cells.
- Edge cells 170 are fine-scale cells having an interface traversing therethrough.
- Node cells 160 are fine-scale cells having portions of at least two interfaces 190 traversing therethrough.
- Internal cells 180 are fine-scale cells free of an interface 190 between adjacent dual coarse-scale cells.
- the sets I i , I e , and I n consist of N i , N e , and N n cells or points, respectively.
- N i , N e , and N n cells or points respectively.
- a ⁇ ⁇ u ⁇ r ⁇ ⁇ ⁇
- the block ⁇ jk represents the effects of the unknowns ⁇ k ⁇ i,e,n ⁇ on the equation corresponding to x j ⁇ I j ⁇ i,e,n ⁇ .
- the sparsity pattern of ⁇ is illustrated in FIG. 2 for the grid depicted in FIG. 1 .
- MSFV operator MSFV matrix
- the j-th column of the N f ⁇ N n matrix B is the basis function (interpolator) relative to the j-th node, which describes the contribution from a unit pressure applied at the node x j ⁇ I n ; whereas the vector C q is the correction function, which describes processes that do not scale with the pressure.
- control-volume summation operator ⁇ is defined, which is represented by a N n ⁇ N f matrix. Each row of ⁇ is associated with a coarse cell 140 : when applied to a vector of size N f , the operator returns a vector of size N n , whose entries are the sums of all values assumed by the original vector in the corresponding coarse cells.
- FIGS. 3 and 4 The sparsity patterns of M and Q for the grid depicted in FIG. 1 is illustrated in FIGS. 3 and 4 . Localization is achieved by removing the internal point-edge point connection when the edge-point equations are solved; the fine scale node-point equations are replaced by coarse-scale equation obtained integrating over the control volumes 150 defined by the coarse grid 110 .
- the last row in Eq. (11) corresponds to the coarse-scale problem defining the node pressure; whereas the upper part of the matrix corresponds to the construction of an approximate solution by superimposition of basis and correction functions.
- the approximate pressure can be obtained as a solution of the MSFV system, Equation (6).
- Constructing the coarse-scale operator, M nn ⁇ B, includes defining the finite-volume summation operator, ⁇ , which is an N n ⁇ N f matrix with entries
- Equation (12) Equation ⁇ ⁇ ( 15 )
- M ee is the reduced-problem operator, Equation (7).
- Equation ⁇ ⁇ ( 16 ) is the corrective function operator (N f ⁇ N f matrix).
- a permutation operator P which replaces the fine-scale lexicographic order by an ordering that is lexicographic in each coarse cell, is naturally associated with the primary coarse grid.
- the permutated or reordered matrix of the grid 110 depicted in FIG. 1 , matrix, ⁇ P
- a P T Equation (17) has a pentadinagonal block structure, if also the coarse cell are in lexicographic order ( FIG. 5 ).
- the original pentadiagonal structure is modified by the permutation operator P into the block pentadiagonal structure.
- Each diagonal block corresponds to a coarse cell ⁇ i ; off diagonal blocks contain the connections between nodes belonging to two distinct but adjacent coarse cells.
- Eq. (19) is equivalent to solving localized problems on the coarse cells with Neumann boundary conditions extracted from the dual pressure.
- the conservative flux field is constructed from the dual pressure gradient (solution of Eq. (6)) across the boundaries of the coarse cells, and from the solution of Eq. (19) elsewhere.
- ⁇ ⁇ M ⁇ 1 q+M ⁇ 1 E ( M ⁇ ) ⁇ ⁇ Equation (21) where the last term estimates a source term for the reduced problems by computing the fluxes generated across the edges by the pressure field ⁇ ⁇ .
- the iterative algorithm in Eq. (25) can be stabilized (and convergence can be accelerated) if the relaxation parameter ⁇ ⁇ is computed by a projection method.
- Projection methods calculate the relaxation parameters based on information on previous residuals: a new approximate solution is sought in a subspace generated by the residual vectors (subspace of solution) and the new residuals are constrained to be orthogonal to a second subspace (subspace of constraint).
- the SD computes a relaxation parameter based on the last residual only, and convergence may be slow. A faster convergence can be obtained if more information from the residual history is used as in the Generalized Minimal Residual method (GMRES). Richardson iterations can be related to GMRES by recursively applying Eq. (25),
- Eq. (28) we have implicitely defined the polynomial p ⁇ 1 (G ⁇ 1 ⁇ ) of degree at most ⁇ 1, whose coefficients can be written in terms of ⁇ ⁇ .
- Equation (30) is the Krylov subspace of degree ⁇ .
- the MSFV iterations can also be stabilized by estimating the transverse fluxes in Eq. (23) from a field ⁇ different from ⁇ ⁇ +1 .
- the internal loop is referred to as the “smoothing iterations”, or simply “smoother”, and the matrix S to as the “smoothing operator”.
- This strategy was recently proposed to improve the accuracy of the MSFV method with Line Relaxation (LR) as the smoother.
- A A x +A y
- a x and A y represent the discretization of the problem in the x and y directions, respectively.
- the LR smoother is obtained by successively applying (A x +diag[A y ]) and (A y +diag[A x ]).
- the MSFV iterations with LR smoother has two important differences from those with Krylov accelerators presented in the previous section.
- This problems can be avoided by combining the smoothing operator, S, and the MSFV iterative operator, M ⁇ 1 Q, in a two-step preconditioner with a Krylov-subspace accelerator. In this case, the local solutions on dual blocks and coarse blocks are alternatively used. Since the smoother operator can be seen to increase the degree of overlapping between dual blocks, we refer to this scheme to as the Overlapping Domain iterations (MSFV-OD).
- the iteration matrices employed in this study are summarized in Table 1.
- Second-step Method Matrix Matrix MSFV Multiscale Finite Volume
- M -1 Q MSFV-OD (Overlapping Domain) D -1 or (D + U) -1 M -1 Q
- Smoothers DAS (Dirichlet Additive Schwartz)
- the computational domains are discretized by a Cartesian grid consisting of 100 ⁇ 100 fine cells; the coarse grid used by the iterative MSFV algorithm consists of 20 ⁇ 20 coarse cells, which corresponds to 5 ⁇ 5 fine per coarse cells.
- the assigned boundary conditions are zero gradient (no-flow) at all domain boundaries; additionally two Dirichlet boundary conditions (fixed pressure) are imposed at two opposite corner cells to create the so called Quarter Five-Spot configuration (QFS).
- FIGS. 6-9 are from a test case with the statistically isotropic permeability field (ISO-field) ( FIGS. 6-7 ) and the anisotropic permeability field (ANISO-field) ( FIGS. 8-9 ) with quarter five spot (QFS) configurations, in which wells are at the top-left and bottom right corners ( FIG. 6 ). Simulations were performed on a 100 ⁇ 100 fine grid 100 , whereas the coarse-scale grid 110 is 20 ⁇ 20.
- the natural logarithm of the statistically isotropic permeability field (ISO-field) follows a normal distribution with mean 0.0 and variance 1.87, and has exponential variogram with correlation length equal to 1/10 of the domain size ( FIG. 6 ).
- the natural logarithm of the statistically anisotropic field also follows a normal distribution with mean 0.0 and variance 1.99, but has anisotropic exponential variogram with the principal axis rotated by sixty (60) degrees with respect to the coordinate axis (the correlation length in the principal-axis direction is 1 ⁇ 5 and 1/100 of the domain size; FIG. 8 ).
- the coefficient matrix, A is constructed from this field by taking the harmonic average of the values in the two adjacent cells.
- FIGS. 7 and 9 The convergence histories of MSFV iterations with Krylov-subspace accelerator (MSFV-GMRES) for the ISO- and ANISO-field are illustrated in FIGS. 7 and 9 , respectively, where we plot the maximum residual, ⁇ tilde over (r) ⁇ ⁇ , as a function of the iterations.
- a regular convergence behavior can be observed, which leads to machine-precision convergence in 40 iterations for the isotropic and 50 iterations for the anisotropic case, respectively.
- No sensitive differences in convergence history have been observed for different location of the source terms (upper-left and lower-right vs. upper-right and lower-left corners): in particular for the ANISO-field, the number of iterations does not significantly depend on the orientation of the main flow with respect to the anisotropy axis corresponding to the longer correlation length.
- MSFV with smoothers The convergence history of the MSFV iterations with smoothers is shown in FIG. 10 , where the performances of different smoothers (DAS, DMS, LR) are compared for a QFS in the ANISO-field. Plotting the maximum residual as functions of total iterations, which is the sum of MSFV iterations (symbols) and smoothing steps (dots). It is apparent that MSFV-DMS needs half of total iterations than MSFV-DAS and MSFV-LR. Although the MSFV operator and the smoothers all have linear complexity, O(N f ), the actual computational costs of an iterations might differ and is strongly dependent on the specific implementation.
- LR is quite appealing because it involves the solution of approximately 2 ⁇ N f 1/2 tri-diagonal problems of size N f 1/2 . Even assuming an optimized implementation (which uses the Thomas algorithm for tri-diagonal systems, 8N f operations, and does not compute the residual explicitly, such that 4N f operations to compute the fluxes and form the right hand side), LR requires 24N f per iterations.
- the N c problems require 67N f operations, which is less than 3 times the cost of LR.
- the computational costs to converge with MSFV-LR and MSFV-DMS are similar (MSFV-LR requires a total work roughly equivalent to 170 iterations of the other operators; a smoothing loop is slightly cheaper than 4 iterations).
- the robust extension of LR to 3D problems is plane relaxation, which requires a 2D multigrid approach in each plane and can lead to a substantial increase in number of iterations.
- MSFV-LR diverges for n s ⁇ 5; MSFV-DAS for n s ⁇ 6; and MSFV-DMS for n s ⁇ 3.
- This shortcoming might be avoided by combining smoothers and Krylov subspace method as in the MSFV-OD, which couples MSFV iterations with DAS or DMS and employs GMRES to accelerate convergence.
- the numerical study of the convergence behavior for anisotropic fields is performed on domains that are discretized by a Cartesian grid consisting of 100 ⁇ 100 fine cells; as before, the coarse grid used by the iterative MSFV algorithm consists of 20 ⁇ 20 coarse cells, which corresponds to 5 ⁇ 5 fine per coarse cells.
- the convergence history is depicted in FIG. 12 and shows that MSFV-GMRES is robust and converges also for highly anisotropic fields, but the number of iterations grows with the anisotropy ratio.
- FIG. 13 the convergence history for MSFV iterations with LR smoother (MSFV-LR) is shown as a function of the total number of iterations, which is the sum of MSFV (circles) and LR smoother steps (solid lines).
- MSFV-LR needs more total iterations to converge for moderately anisotropic problems, but the number of total iterations does not drastically grow for very anisotropy ratios. The reason is that, in the latter case, the exact solution tends to become one-dimensional and the convergence rate of MSFV-LR, which exactly solves one-dimensional problems, stops deteriorating. Therefore, LR appears as an excellent smoother for Cartesian grids with anisotropy axis aligned with the grid axis, but its convergence rate deteriorates if the anisotropy axis are not aligned with the lines of relaxation, as it is demonstrate in FIGS. 10 and 11 .
- Impermeable shale layers in order to consider fields with characteristics similar to those encountered in subsurface flow applications, two additional fields mimic the presence of impermeable structures (shale layers) or permeable meanders, which produce tortuous flow fields.
- the first field (SHALE-field) is a binary field consisting of multiple, intersecting layers embedded in a 10 6 -times more transmissive matrix and acting as flow barriers ( FIG. 14A ).
- the field is represented on a 125 ⁇ 125 grid and a 25 ⁇ 25 coarse grid is employed for MSFV simulations, which corresponds to 5 ⁇ 5 fine per coarse cells.
- the assigned boundary conditions create the QFS flow configuration: zero-gradient (no-flux) conditions are imposed at the domain boundary, and Dirichlet conditions 1 and 0 at the lower-left and upper-right corner cells, respectively.
- the solution obtained with the original MSFV method (without iterations) is depicted in FIG. 14C , which exhibits unphysical peaks in correspondence of the impermeable layers. By iterating, these peaks are removed and the algorithm converges to the exact solution ( FIG. 14D ).
- the convergence history is depicted in FIG. 14B and shows that convergence is achieved in about 40 iterations if MSFV-GMRES is employed.
- the convergence rate can be improved if the MSFV operator is combined with the Schwarz overlap smoothers (MSVF-OD), but this comes at additional computational costs, because two preconditioners now have to be applied at each iteration.
- restart can be used to limit the increase in memory and computational costs of GMRES.
- the effect of restart on the performance of MSFV-GMRES and MSVF-OD is illustrated by the convergence histories in FIGS. 18 and 19 . Although the number of iterations required to converge increases, the iterative scheme is stable and converges to the exact solution for small restart parameter.
- An embodiment provides a natural and robust iterative MSFV method or algorithm to improve the quality of previous MSFV solutions in numerically challenging test cases: the boundary conditions assigned to solve the local problems on dual cells are iteratively improved, leading to a more accurate localization assumption.
- the MSFV method relies only on the data structure of the MSFV method (which consists of a primary and dual coarse grids) to construct appropriate iterative operators, and employs a Krylov-subspace projection method (GMRES) to obtain an unconditionally stable algorithm.
- GMRES Krylov-subspace projection method
- a natural converging scheme can be constructed based on the MSFV operator only (MSFV-GMRES): in this case, the localization is improved by estimating the fluxes transversal to dual-cell boundaries directly from the previous approximate solution.
- An alternative embodiment is to estimate the transverse fluxes from local solutions computed on the block of the primary coarse grid (MsVF-OD).
- MsVF-OD primary coarse grid
- the MSFV operator is combined in a two step method with an operator derived from the problem solved to construct the conservative flux field. This method takes advantage of shift between coarse and dual grids to obtain better information on the flow near dual-cell boundaries. Therefore, it improves the solution by indirectly increasing the degree of overlapping.
- the method can be regarded as a linear solver.
- the MSFV operator is a one cell overlap, domain-decomposition preconditioner, whose relationship with the Schur complement has been recently demonstrated. The very peculiar characteristic of this operator is that at any iteration the residual is zero and the solution is conservative on the coarse grid.
- One of the two embodiments presented is a two-step preconditioner that couples the MSFV operator with a Schwarz-overlap operator. Since a MSFV-OD iteration has approximately double cost with respect to a MSFV iteration, both methods require a similar computational effort to converge, even if MSFV-OD converges in about half iterations.
- Matrix Operators The matrix operators defined in the MSFV Iterations section can have the following properties:
- ⁇ ⁇ RR T I nn 2.
- ⁇ ⁇ R T ⁇ R [ 0 0 0 0 0 0 0 I nn ] 3.
- ⁇ ⁇ ⁇ ⁇ A ⁇ ⁇ M - 1 ⁇ E ⁇ ⁇ A ⁇ ( B ( ⁇ ⁇ ⁇ A ⁇ ⁇ B ) - 1 ⁇ R + C )
- FIG. 20 illustrates a system 300 that can be used in simulating a fine-scale geological model of a subsurface reservoir as described by the multiscale finite volume method above.
- System 300 includes user interface 310 , such that an operator can actively input information and review operations of system 300 .
- User interface 310 can be any means in which a person is capable of interacting with system 300 such as a keyboard, mouse, or touch-screen display.
- Input that is entered into system 300 through user interface 310 can be stored in a database 320 .
- any information generated by system 300 can also be stored in database 320 .
- database 320 can store user-defined parameters, as well as, system generated computed solutions.
- geological models 321 , coarse-scale operators 323 , computed pressure solutions 325 , and computed velocity field solutions 327 are all examples of information that can be stored in database 320 .
- System 300 includes software 330 that is stored on a processor readable medium.
- a processor readable medium include, but are not limited to, an electronic circuit, a semiconductor memory device, a ROM, a flash memory, an erasable programmable ROM (EPROM), a floppy diskette, a compact disk (CD-ROM), an optical disk, a hard disk, and a fiber optic medium.
- software 330 can include a plurality of modules for performing system tasks such as performing the multiscale finite volume method previously described herein.
- Processor 340 interprets instructions to execute software 330 , as well as, generates automatic instructions to execute software for system 300 responsive to predetermined conditions. Instructions from both user interface 310 and software 330 are processed by processor 340 for operation of system 300 .
- a plurality of processors can be utilized such that system operations can be executed more rapidly.
- modules for software 330 include, but are not limited to, coarse-scale operator module 331 and computation module 333 .
- Coarse-scale operator module 331 is capable of constructing coarse-scale operator 323 .
- Computation module 333 is capable of computing pressure in the dual coarse-scale cells responsive to coarse-scale operator 323 .
- Computation module 333 is also capable of computing pressure in the primary coarse-scale cells responsive to the pressure in the dual coarse-scale cells. Pressures in the dual coarse-scale cells and primary coarse-scale cells are examples of computed pressures 325 that can be stored in database 320 .
- computation module 333 computes a conservative velocity field from the pressure in the primary coarse-scale cells.
- the conservative velocity field is an example of a computed velocity field 327 that can be stored in database 320 . Accordingly, computation module 333 is able to compute any of the computational steps of the iterative multiscale methods described herein, such as pressures and conservative flux fields while accounting for transverse fluxes between the dual coarse-scale cells such as between edge cells.
- system 300 can include reporting unit 350 to provide information to the operator or to other systems (not shown).
- reporting unit 350 can be a printer, display screen, or a data storage device.
- system 300 need not include reporting unit 350 , and alternatively user interface 310 can be utilized for reporting information of system 300 to the operator.
- Communications network 360 can be any means that allows for information transfer. Examples of such a communications network 360 presently include, but are not limited to, a switch within a computer, a personal area network (PAN), a local area network (LAN), a wide area network (WAN), and a global area network (GAN). Communications network 360 can also include any hardware technology used to connect the individual devices in the network, such as an optical cable or wireless radio frequency.
- PAN personal area network
- LAN local area network
- WAN wide area network
- GAN global area network
- Communications network 360 can also include any hardware technology used to connect the individual devices in the network, such as an optical cable or wireless radio frequency.
- an operator initiates software 330 , through user interface 310 , to perform the multiscale finite volume method.
- Outputs from each software module can be stored in database 320 .
- Software 330 utilizes coarse-scale operator module 331 to construct coarse-scale operator 323 .
- the computation module 333 can retrieve coarse-scale operator 323 from either database 320 or directly from coarse-scale operator module 331 and compute the pressure in the dual coarse-scale cells.
- Computation module 333 also computes a conservative flux field while accounting for transverse fluxes between the dual coarse-scale cells, such as between edge cells, based on the pressure in the dual coarse-scale cells.
- a visual display can be produced using the conservative flux field. For example, pressure distributions, velocity fields, or fluid flow within the reservoir can be displayed.
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Abstract
Description
Au=r Equation (1)
in the unknown u=[u1 u2 . . . uj . . . UN
I f=I i∪I e∪I n Equation (2)
ũ={tilde over (P)}u=[ũiũeũn]T and {tilde over (r)}={tilde over (P)}r=[{tilde over (r)}i{tilde over (r)}e{tilde over (r)}n]T Equation (3)
where ũk=[ũ(xjεIk)]T and {tilde over (r)}k=[{tilde over (r)}(xjεIk)]T, with kε{i,e,n}. Consider Cartesian coarse grids and use the standard natural (alphabetic) reordering: dual cells, dual boundaries and dual nodes are ordered lexicographically, and so are ordered their internal elements.
is the reordered coefficient matrix. The block Ãjk represents the effects of the unknowns ũkε{i,e,n} on the equation corresponding to xjεIjε{i,e,n}. The sparsity pattern of à is illustrated in
Mũ=q Equation (6)
where M is the MSFV dual pressure operator (hereafter “MSFV operator” or “MSFV matrix”), which can be defined block-wise (analogously to à in Eq. 5); and q the appropriate source term vector.
M ee =à ee+diag[Σi à ie T] Equation (7)
which is the “reduced-problem” operator—where the operator “diag[•]” transforms a vector into a diagonal matrix.
ũ=Bũ n +Cq Equation (8)
Mnn=χÃB Equation (9)
which is the lower diagonal block in M, and the coarse-scale right hand side of the equation becomes
q n=χ({tilde over (r)}−ÃCq) Equation (10)
and the right hand side of the equation is
q=(I−R T R−R T χÃC+R Tχ){tilde over (r)}=(E+R Tχ){tilde over (r)}=Q{tilde over (r)} Equation (12)
where the operators are defined Q=E+RTχ, E=I−RTR−RTχÃC, R=[0 0 Inn], and Inn and I are an Nn×Nn and Nf×Nf identity matrix, respectively. The sparsity patterns of M and Q for the grid depicted in
M −1 =BM nn −1 R+C Equation (13)
and the basis-function operator, the Nf×Nn matrix
where Mee is the reduced-problem operator, Equation (7). Additionally, the right hand side, Equation (12) includes the definition of the operator E=I−RTR−RTÃC where
is the corrective function operator (Nf×Nf matrix).
Ā=
has a pentadinagonal block structure, if also the coarse cell are in lexicographic order (
Ā=D+U+L Equation (18)
where D, U, and L are block diagonal, strictly block upper triangular, and strictly block lower triangular matrices, respectively; then we consider the solution of the problem
D′ū=
where
D′=D+diag[Σj(Ā jk −D jk)] Equation (20)
ũ μ =M −1 q+M −1 E(M−Ã)ũ ν Equation (21)
where the last term estimates a source term for the reduced problems by computing the fluxes generated across the edges by the pressure field ũν. The transverse fluxes are calculated from the difference between the two matrices, which gives the connections removed in the generation of M and is derived using the equivalence E(M+RT(Inn−Mnn)R−Ã)=E(M−Ã) which follows from the property ERT=0 (see Matrix Operators section herein).
ũ μ =ũ ν +M −1 {q+[(E−I)M−EÃ]ũ ν }=ũ ν +M −1 Q({tilde over (r)}−Ãũ ν) Equation (22)
where the property (E−I)M=−RTχà (see Matrix Operators section herein), is used together with Eq. (12) and the definition Q=E+RTχ. Eq. (22) can be generalized by introducing the relaxation parameter ων, i.e.,
ũ μ =ũ ν+ων M −1 Q({tilde over (r)}−Ãũ ν) Equation (23)
which has the important property that ũμ satisfies coarse-scale mass balance if ũν or ων are appropriately chosen. Indeed, we have
χ({tilde over (r)}−Ãũ μ)=(χ−ων χÃM −1 Q)({tilde over (r)}−Ãũ ν)=(1−ων)χ({tilde over (r)}−Ãũ ν) Equation (24)
where we have used Eq. (23), and the properties χÃM−1E=0 and χÃM−1RT=Inn (see Matrix Operators section herein). If ũν is conservative on the coarse grid, ũμ is also conservative for any value of the relaxation parameter ων; on the other hand, if ων=1, ũμ is conservative for arbitrary choice of ũν.
ũ ν+1 =ũ ν+ων G −1εν Equation (25)
where we have defined
G=Q −1 M Equation (26)
and the residual
εν ={tilde over (r)}−Ãũ ν Equation (27)
in order to make notation more compact, and ν≧0 denotes the iteration level and ũ0=M−1q is the standard MSFV solution. From the sparsity patterns of Q−1 and M (
with initial guess ũ0=M−1q. In Eq. (28) we have implicitely defined the polynomial pν−1(G−1Ã) of degree at most ν−1, whose coefficients can be written in terms of ων−σ. Eq. (28) delivers a solution ũνεũ0+κν(G−1ε0, G−1Ã), since
pν−1(G−1Ã)G−1ε0εκν(G−1ε0, G−1Ã) Equation (29)
where
κν(G −1ε0 , G −1 Ã)=span{G −1ε0, (G −1 Ã)G −1ε0, (G −1 Ã)2 G −1ε0, . . . , (G −1 Ã)ν−1 G −1ε0} Equation (30)
is the Krylov subspace of degree ν. GMRES computes the coefficients of pν−1(G−1Ã) such that the l2-norm of the preconditioned residual is a minimum or, equivalently, that G−1εν+1⊥G−1Ãκm(G−1εν, G−1Ã), which is equivalent to considering the preconditioned system of the form
G −1 Ã=M −1 QÃũ=M −1 Q{tilde over (r)}=G −1 {tilde over (r)} Equation (31)
QÃM −1 {tilde over (ν)}=Q{tilde over (r)}, {tilde over (ν)}=Mũ Equation (32)
TABLE 1 |
Summary of the iteration matrices employed. |
Second-step | ||
Method | Matrix | Matrix |
MSFV (Multiscale Finite Volume) | M-1Q | — |
MSFV-OD (Overlapping Domain) | D-1 or (D + U)-1 | M-1Q |
Smoothers: | ||
DAS (Dirichlet Additive Schwartz) | D-1 | — |
DMS (Dirichlet Multiplicative Schwartz) | (D + U)-1 | — |
LR (Line Relaxation) | (Ax + diag[Ay])-1 | (Ay + diag[Ax])-1 |
Claims (21)
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